Attention mechanisms are at the core of transformer architectures, enabling models to capture relationships within and across sequences. Two critical attention types are Self-Attention and ...

In this blog, we’ll break down how Transformers generate tokens step-by-step, explore the Encoder-Decoder architecture, dive into positional encoding, and explain how LoRA (Low-Rank Adaptation) ...

Modern systems for automatic speech recognition, including the RNN-Transducer and Attention-based Encoder-Decoder (AED), are designed so that the encoder is not required to alter the time-position of ...

Facial Emotion Recognition (FER) has emerged as an essential task in affective computing, with a wide range of utilization from man-machine interaction to health monitoring. A novel technique of FER ...

We propose a method for anomaly localization in industrial images using Transformer Encoder-Decoder Mask Reconstruction. The self-attention mechanism of the Transformer enables better attention to ...